Uncovering Reward Hacks How AI Discovers Loopholes in Robotic Systems

After implementing three different approaches for the same PCB testing system - traditional robotics, supervised ML, and reinforcement learning - clear patterns emerged about their strengths and limitations. Traditional methods excel in predictable environments but struggle with adaptation. Supervised ML handles visual perception well but still needs explicit motion programming. Reinforcement learning integrates perception and control learning but demands more development resources. This comparison provides practical insights into which approach fits different robotics challenges, with a surprising conclusion about how they can work together.

March 30, 2025 5 minutes